Implementation of MPI in Python and comparison with other parallel programming techniques
High performance computing becomes more important in many areas by provide fast, reliable and cost effective solutions in many applications. Availability of multi-core system in various platforms ranging from desktop computer to supercomputer enable parallelism to be exploiting by users. Paral...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2015
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| Online Access: | http://psasir.upm.edu.my/id/eprint/66644/1/FSKTM%202015%2030%20IR.pdf http://psasir.upm.edu.my/id/eprint/66644/ |
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| Summary: | High performance computing becomes more important in many areas by
provide fast, reliable and cost effective solutions in many applications. Availability of
multi-core system in various platforms ranging from desktop computer to supercomputer
enable parallelism to be exploiting by users. Parallel programming provides access to
users to optimize resources by applying multi-threading or multi-processing techniques
in application development. One of the popular approaches in parallel programming is
message passing which is widely used in both distributed and shared memory
architecture.
Python is a powerful open source programming language that popular among scientific
computing committee. It provides flexibility and space for skilled users to create their
own environment, and appeal beginners with its object-oriented programming. Python
also support parallel programming by adapting message passing paradigm into its
language. Few of its MPI implementations are pyMPI, Pypar, MPI for Python (mpi4py)
and pypvm. MPI for Python is complies with MPI-2 specification and can be used with
other Python modules such as NumPy and Cython to exploit multiple processors.
This project will provide detail analysis of current implementation of message passing
paradigm in Python and compare it with other popular parallel programming technique.
Therefore, it aims to produce a good reference to users especially beginners in
developing parallel applications. |
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